Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/142488 |
Resumo: | The aim this study was quantify the calorific power of 111 gasoline samples available at filling stations using near infrared spectroscopy in conjunction with the multivariate regression. The calorific power value of the fuels was determined using an adiabatic bomb calorimeter (norm ASTM D 4.809). For the construction of multivariate regression models were used 2/3 of the samples for calibration and the remainder to prediction, using the interval partial least squares (iPLS) and synergy interval partial least square (siPLS) algorithms. In the best iPLS model was selected the spectral range from 5561 to 6650 cm-1, obtaining RMSEP of 102 g cal-1 and showing a correlation coefficient (r) of 0.8218 and 0.71% to calibration errors and 0.47% for prediction errors. The siPLS model divided into 32 intervals and grouped into three intervals was the highlighted model, which selected the region below 6000 cm-1 and above 6500 cm-1 with, presenting values of RMSECV of 89.8 cal g-1 and RMSEP of 96.7 cal g-1, and correlation coefficients for the cross-validation and prediction of 0.7834 and 0.7293, respectively. The methodology proposed in this work is efficient, with prediction errors lower than 1%, being a clean alternative, fast, safe and practical. |
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Francesquett, Janice ZulmaDopke, Henrique BeckerCosta, Adilson Ben daKipper, Liane MahlmannFerrão, Marco Flôres2016-06-10T02:09:32Z20131984-6428http://hdl.handle.net/10183/142488000895013The aim this study was quantify the calorific power of 111 gasoline samples available at filling stations using near infrared spectroscopy in conjunction with the multivariate regression. The calorific power value of the fuels was determined using an adiabatic bomb calorimeter (norm ASTM D 4.809). For the construction of multivariate regression models were used 2/3 of the samples for calibration and the remainder to prediction, using the interval partial least squares (iPLS) and synergy interval partial least square (siPLS) algorithms. In the best iPLS model was selected the spectral range from 5561 to 6650 cm-1, obtaining RMSEP of 102 g cal-1 and showing a correlation coefficient (r) of 0.8218 and 0.71% to calibration errors and 0.47% for prediction errors. The siPLS model divided into 32 intervals and grouped into three intervals was the highlighted model, which selected the region below 6000 cm-1 and above 6500 cm-1 with, presenting values of RMSECV of 89.8 cal g-1 and RMSEP of 96.7 cal g-1, and correlation coefficients for the cross-validation and prediction of 0.7834 and 0.7293, respectively. The methodology proposed in this work is efficient, with prediction errors lower than 1%, being a clean alternative, fast, safe and practical.application/pdfporOrbital : the electronic journal of chemistry. Mato Grosso do Sul. Vol. 5, n. 2 (Apr./June 2013), p. 88-95GasolinaEspectroscopia no infravermelhoAnálise multivariadaGasolineCalorific powerInfraredMultivariate regressionDeterminação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariadainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000895013.pdf000895013.pdfTexto completoapplication/pdf239717http://www.lume.ufrgs.br/bitstream/10183/142488/1/000895013.pdf734527692715047ec6ce33f4b2cdd39bMD51TEXT000895013.pdf.txt000895013.pdf.txtExtracted Texttext/plain26348http://www.lume.ufrgs.br/bitstream/10183/142488/2/000895013.pdf.txta86ef31aa92361eb58021457becb6446MD52THUMBNAIL000895013.pdf.jpg000895013.pdf.jpgGenerated Thumbnailimage/jpeg2022http://www.lume.ufrgs.br/bitstream/10183/142488/3/000895013.pdf.jpged2f8a49fb58e5b776b020529dd98900MD5310183/1424882018-10-26 09:55:03.379oai:www.lume.ufrgs.br:10183/142488Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-26T12:55:03Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
title |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
spellingShingle |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada Francesquett, Janice Zulma Gasolina Espectroscopia no infravermelho Análise multivariada Gasoline Calorific power Infrared Multivariate regression |
title_short |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
title_full |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
title_fullStr |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
title_full_unstemmed |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
title_sort |
Determinação do poder calorífico de amostras de gasolina utilizando espectroscopia no infravermelho próximo e regressão multivariada |
author |
Francesquett, Janice Zulma |
author_facet |
Francesquett, Janice Zulma Dopke, Henrique Becker Costa, Adilson Ben da Kipper, Liane Mahlmann Ferrão, Marco Flôres |
author_role |
author |
author2 |
Dopke, Henrique Becker Costa, Adilson Ben da Kipper, Liane Mahlmann Ferrão, Marco Flôres |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Francesquett, Janice Zulma Dopke, Henrique Becker Costa, Adilson Ben da Kipper, Liane Mahlmann Ferrão, Marco Flôres |
dc.subject.por.fl_str_mv |
Gasolina Espectroscopia no infravermelho Análise multivariada |
topic |
Gasolina Espectroscopia no infravermelho Análise multivariada Gasoline Calorific power Infrared Multivariate regression |
dc.subject.eng.fl_str_mv |
Gasoline Calorific power Infrared Multivariate regression |
description |
The aim this study was quantify the calorific power of 111 gasoline samples available at filling stations using near infrared spectroscopy in conjunction with the multivariate regression. The calorific power value of the fuels was determined using an adiabatic bomb calorimeter (norm ASTM D 4.809). For the construction of multivariate regression models were used 2/3 of the samples for calibration and the remainder to prediction, using the interval partial least squares (iPLS) and synergy interval partial least square (siPLS) algorithms. In the best iPLS model was selected the spectral range from 5561 to 6650 cm-1, obtaining RMSEP of 102 g cal-1 and showing a correlation coefficient (r) of 0.8218 and 0.71% to calibration errors and 0.47% for prediction errors. The siPLS model divided into 32 intervals and grouped into three intervals was the highlighted model, which selected the region below 6000 cm-1 and above 6500 cm-1 with, presenting values of RMSECV of 89.8 cal g-1 and RMSEP of 96.7 cal g-1, and correlation coefficients for the cross-validation and prediction of 0.7834 and 0.7293, respectively. The methodology proposed in this work is efficient, with prediction errors lower than 1%, being a clean alternative, fast, safe and practical. |
publishDate |
2013 |
dc.date.issued.fl_str_mv |
2013 |
dc.date.accessioned.fl_str_mv |
2016-06-10T02:09:32Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
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publishedVersion |
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http://hdl.handle.net/10183/142488 |
dc.identifier.issn.pt_BR.fl_str_mv |
1984-6428 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000895013 |
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1984-6428 000895013 |
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http://hdl.handle.net/10183/142488 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.ispartof.pt_BR.fl_str_mv |
Orbital : the electronic journal of chemistry. Mato Grosso do Sul. Vol. 5, n. 2 (Apr./June 2013), p. 88-95 |
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openAccess |
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